CN108414475B - LIBS analysis method based on optical chromatography simultaneous iterative reconstruction - Google Patents
LIBS analysis method based on optical chromatography simultaneous iterative reconstruction Download PDFInfo
- Publication number
- CN108414475B CN108414475B CN201810091015.1A CN201810091015A CN108414475B CN 108414475 B CN108414475 B CN 108414475B CN 201810091015 A CN201810091015 A CN 201810091015A CN 108414475 B CN108414475 B CN 108414475B
- Authority
- CN
- China
- Prior art keywords
- matrix
- libs
- standard sample
- elements
- values
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
Abstract
The invention discloses an LIBS analysis method based on optical tomography and simultaneous iterative reconstruction, which is characterized in that a multivariate LIBS quantitative solving problem is equivalent to a high-precision optical tomography reconstruction problem, and the solving of a correlation matrix F is equivalent to the reconstruction of three-dimensional physical quantity distribution in the optical tomography. And (3) adopting a high-precision SIRT iterative algorithm in combination with matrix column vector decomposition to iteratively solve each column vector of the incidence matrix F column by column so as to obtain the incidence matrix F. And then realizing high-precision solving analysis of each element in the unknown sample according to the operation of the actually measured normalized spectral intensity vector of the unknown sample and the incidence matrix. The invention has the advantages that the optical chromatography-like model is adopted and solved in multivariate analysis and calibration so as to solve the influence of competitive emission of different elements in the chemical matrix effect; and solving by adopting an SIRT chromatography iterative algorithm, effectively inhibiting noise in the measured data, and obtaining a correlation matrix with the minimum root mean square error, thereby improving the LIBS quantitative analysis precision.
Description
Technical Field
The invention relates to a laser spectrum detection method, in particular to a quantitative laser-induced breakdown spectroscopy analysis method, which is suitable for simultaneous quantitative solution and analysis of multiple elements of a detection target and belongs to the field of photoelectric detection.
Background
Laser-induced breakdown spectroscopy (LIBS) is an atomic analysis spectroscopy technique. The nanosecond short pulse laser is adopted to focus the surface of a target, the target is degraded at an instantaneous high temperature to generate plasma, atoms and ions fall back from a high-energy state to a low-energy state in a cooling process to emit spectral lines containing element information, and the nanosecond short pulse laser can be used for detecting material composition elements. The LIBS technology can well realize qualitative analysis of target elements, but the quantitative analysis is influenced by chemical matrix effect, namely emission lines of certain elements with the same content are different when the certain elements are placed in different matrixes.
In order to solve the influence of the chemical matrix effect on the LIBS quantitative analysis, a multivariate analysis method can be adopted, namely, a related equation is established according to the relation between a plurality of spectral lines of a plurality of elements and the content of the spectral lines, and the content of the plurality of elements of the target to be detected is obtained by solving a multivariate mathematical matrix equation. The method has the advantages that the influence of the chemical matrix effect on the quantitative analysis precision can be eliminated to a certain extent, and the disadvantage that the precision of the solving algorithm depends on the design of the multi-element calibration mathematical model and the solving algorithm in the previous period.
The solving problem of LIBS multivariate quantitative analysis has similarity with the solving problem of optical tomography reconstruction under the condition of few projection directions, and the accuracy of the optical tomography reconstruction is higher compared with the LIBS quantitative analysis. Meanwhile, each pixel reconstructed in the Iterative Reconstruction technology (SIRT) is corrected after all projection values corresponding to each ray are calculated, and noise in the measured data can be effectively suppressed. Therefore, the high-precision optical tomography reconstruction model is used for solving the problem of multivariate LIBS quantification. And (3) combining a high-precision SIRT iterative algorithm in optical chromatography with matrix column vector decomposition, and iteratively solving column by column to obtain a correlation matrix between the normalized spectral intensity matrix of the standard sample and the content of each element. And then realizing high-precision solving analysis of each element in the unknown sample according to the operation of the actually measured normalized spectral intensity vector of the unknown sample and the incidence matrix, and solving the problem of solving precision of LIBS multivariate analysis.
Disclosure of Invention
The invention aims to provide a multi-element analysis solving method of a multi-element LIBS matrix, which comprises the steps of firstly establishing a matrix mathematical model of LIBS multi-element analysis similar to optical tomography reconstruction, then solving an association vector based on SIRT iterative algorithm in optical tomography in combination with matrix column vector decomposition to obtain an association matrix, and obtaining atomic fractions of a plurality of elements of a target to be detected according to the operation of normalized spectral intensity and the association matrix of the target to be detected so as to realize high-precision quantitative LIBS detection.
The present invention is achieved in such a way that,
1. assume that the number of elements to be quantitatively analyzed (i.e., element dimensions) is M and are well-ordered. N standard samples were prepared for calibration (i.e. sample dimension N). The N samples are solid, have equal size and size, contain the M elements in different proportions, the atomic fraction (atomic number percentage) of each element is known, and the components in each sample are uniformly distributed;
2. constructing a multivariate LIBS quantitative analysis matrix equation, namely a projection matrix, a flow field physical quantity image matrix and a measurement matrix in optical tomography reconstruction
WF＝P
In the formula, W is a normalized spectral intensity matrix of a standard sample, which is equivalent to a projection matrix in optical tomography reconstruction; f is a correlation matrix, which is equivalent to a flow field physical quantity image matrix in optical tomography reconstruction, and P is a standard sample atomic fraction matrix, which is equivalent to a measurement matrix;
3. the normalized spectral intensity matrix W of the standard sample is constructed according to the following method:
and performing LIBS detection on the N standard samples under the same test conditions and test parameters to obtain N LIBS spectrograms corresponding to the N standard samples, and performing normalization processing on the N LIBS spectrograms to obtain N normalized LIBS spectrograms. Respectively taking k characteristic spectral lines (requiring the dimension N of the sample to be larger than the spectral dimension kM) for each element, and constructing a standard sample normalized spectral intensity matrix W with N rows by kM columns as follows:
the kM values in the first row in the normalized spectral intensity matrix represent the normalized spectral intensity values of the spectral lines represented by the M elements kM of the first standard sample; the kM values in the second row represent the normalized spectral intensity values of the M elements kM of the second standard sample representing the spectral lines; and so on …; kM values in the nth row represent the normalized spectral intensity values of the spectral lines for the nth standard sample M elements kM;
4. constructing a standard sample atomic fraction matrix P with N rows by M columns as follows:
the M values in the first row of the atomic fraction matrix represent the atomic fractions of the M elements of the first standard sample; the M values in the second row represent the atomic fractions of the M elements of the second standard sample; and so on …; the M values in the Nth row represent the atomic fractions of the M elements of the Nth standard sample;
5. the correlation matrix F, which reflects the correlation between W and P, can be expressed as:
the incidence matrix F is a matrix of kM rows by M columns, and the required solution kM2The F matrix can be obtained by the unit value. Column decomposing the incidence matrix F into M incidence vectors F1、F2、F3、...、FM(ii) a Performing column decomposition on the atomic fraction matrix P of the standard sample into M atomic fraction vectors P1、P2、P3、...、PM；
6. Converting the solution of the incidence matrix F into M incidence vectors F1、F2、F3、...、FMThe solution model is as follows:
Pi＝WFi+Ei
wherein i is 1,2,3iAs error vector, at N>In the case of kM, for FiThe solution of the correlation vector F is the solution of the over-determined equation, the error is minimum based on a certain optimization criterion, namely the optimal approximate solution under the optimization criterion is obtained, and the SIRT iterative algorithm based on the least square criterion is adopted to carry out the correlation vector FiAnd (3) solving:
Fi 0＝WTPi
in the above formula, the superscript 0 represents the initial value; superscript T represents transposition; the superscript q represents the q-th iteration value; superscript q +1 represents the q +1 th iteration value; λ is a relaxation factor, and the magnitude of the value of λ represents the degree of tightness of the iterative constraint;
the termination conditions for the iteration are:
|Fi q+1-Fi q|2＜ε
ε is a very small number, which in this example is 0.001; after the iteration has terminated, FiThe last iteration value is FiThe solution result of (2);
7. all M relevance vectors FiAfter solving is completed, obtaining a correlation matrix F; and (3) carrying out LIBS detection on the target to be detected under the same test conditions as the N standard samples to obtain an LIBS spectrogram, and carrying out normalization processing on the LIBS spectrogram to obtain a normalized LIBS spectrogram of the sample to be detected. Obtaining a normalized spectral intensity vector of M element kM representative spectral lines of the target to be detected:
D＝[d1,d2,d3,...,dkM]
calculating the atomic fractions of M elements of the target to be detected according to the following formula:
the invention has the advantages that the optical chromatography-like model is adopted and solved in multivariate analysis and calibration so as to solve the influence of competitive emission of different elements in the chemical matrix effect; and solving by adopting an SIRT chromatography iterative algorithm, effectively inhibiting noise in the measured data, and obtaining a correlation matrix with the minimum root mean square error, thereby improving the LIBS quantitative analysis precision.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention.
Detailed Description
The invention aims to provide a multivariate LIBS quantitative analysis solving method, which is characterized in that a high-precision optical chromatography reconstruction model is used for modeling a multivariate LIBS quantitative solving problem, and a relation among a normalized spectral intensity matrix W of a standard sample, an incidence matrix F and an atomic fraction matrix P of the standard sample is established; and (3) adopting a high-precision SIRT iterative algorithm in optical chromatography in combination with matrix column vector decomposition to iteratively solve each column vector of the correlation matrix F column by column so as to obtain the correlation matrix F reflecting the mutual relation between W and P. And then realizing high-precision solving analysis of each element in the unknown sample according to the operation of the actually measured normalized spectral intensity vector of the unknown sample and the incidence matrix, and solving the problem of solving precision of LIBS multivariate analysis.
The following specific examples illustrate the present LIBS quantitative analysis method:
1. assuming that the number of elements to be quantitatively analyzed is 12 (i.e. taking the element dimension M ═ 12), including sodium, magnesium, calcium, iron, manganese, copper, silicon, carbon, oxygen, sulfur, nitrogen and hydrogen, in the order from 1 to 12. 100 standard samples were prepared for calibration (i.e., sample dimension N is taken as 100). The 100 samples are solid, have equal size and size, contain the twelve elements in different proportions, the atomic fraction (atomic number percentage) of each element is known, and the components in each sample are uniformly distributed;
2. constructing a multivariate LIBS quantitative analysis matrix equation shown in figure 1 by referring to the relationship among a projection matrix, a flow field physical quantity image matrix and a measurement matrix in optical tomography reconstruction, namely
WF＝P
In the formula, W is a normalized spectral intensity matrix of a standard sample, which is equivalent to a projection matrix in optical tomography reconstruction; f is a correlation matrix, which is equivalent to a flow field physical quantity image matrix in optical tomography reconstruction, and P is a standard sample atomic fraction matrix, which is equivalent to a measurement matrix;
3. the normalized spectral intensity matrix W of the standard sample is constructed according to the following method:
LIBS detection is carried out on the 100 standard samples under the same test conditions and test parameters to obtain 100 LIBS spectrograms corresponding to the 100 standard samples, and normalization processing is carried out on the 100 LIBS spectrograms to obtain 100 normalized LIBS spectrograms. Taking 3 characteristic spectral lines for each element (i.e. k is 3, which requires that the sample dimension N > the spectral dimension kM, in this example, the sample dimension N is 100, which is greater than the spectral dimension kM is 36), a normalized spectral intensity matrix W of the standard sample is constructed, which has N rows by kM columns (in this example, 100 rows by 36 columns) as follows:
the kM values in the first row in the normalized spectral intensity matrix represent the normalized spectral intensity values of the spectral lines represented by the M elements kM of the first standard sample; the kM values in the second row represent the normalized spectral intensity values of the M elements kM of the second standard sample representing the spectral lines; and so on …; kM values in the nth row represent the normalized spectral intensity values of the spectral lines for the nth standard sample M elements kM;
4. constructing a standard sample atomic fraction matrix P with N rows by M columns as follows:
in practice, M is 12 and N is 100. The M values in the first row of the atomic fraction matrix represent the atomic fractions of the M elements of the first standard sample; the M values in the second row represent the atomic fractions of the M elements of the second standard sample; and so on …; the M values in the Nth row represent the atomic fractions of the M elements of the Nth standard sample;
5. the correlation matrix F, which reflects the correlation between W and P, can be expressed as:
the correlation matrix F is a matrix of kM rows by M columns, in the specific embodiment a 36 row by 12 matrix. Need to solve for kM2432 cell values, the F matrix can be obtained.As shown in fig. 1, the correlation matrix F is column-decomposed into M correlation vectors F1、F2、F3、...、FM(ii) a Performing column decomposition on the atomic fraction matrix P of the standard sample into M atomic fraction vectors P1、P2、P3、...、PM；
6. Converting the solution of the incidence matrix F into M incidence vectors F1、F2、F3、...、FMThe solution model is as follows:
Pi＝WFi+Ei
wherein i is 1,2,3iAs error vector, at N>In the case of kM, for FiThe solution of the correlation vector F is the solution of the over-determined equation, the error is minimum based on a certain optimization criterion, namely the optimal approximate solution under the optimization criterion is obtained, and the SIRT iterative algorithm based on the least square criterion is adopted to carry out the correlation vector FiAnd (3) solving:
Fi 0＝WTPi
Fi q+1＝Fi q+λ·WT(Pi-WFi q)
in the above formula, the superscript 0 represents the initial value; superscript T represents transposition; the superscript q represents the q-th iteration value; superscript q +1 represents the q +1 th iteration value; λ is a relaxation factor, the value of λ is between 0 and 2, the magnitude of the value represents the degree of tightness of the iterative constraint, and the value is 0.5 in the embodiment;
the termination conditions for the iteration are:
|Fi q+1-Fi q|2＜ε
ε is a very small number, which in this example is 0.001; after the iteration has terminated, FiThe last iteration value is FiThe solution result of (2);
7. all M relevance vectors FiAfter solving is completed, obtaining a correlation matrix F; LIBS detection is carried out on the target to be detected under the same test conditions as the N standard samples to obtain a LIBS spectrogram, and normalization processing is carried out on the LIBS spectrogramAnd obtaining a normalized LIBS spectrogram of the sample to be detected. Obtaining a normalized spectral intensity vector of M element kM representative spectral lines of the target to be detected:
D＝[d1,d2,d3,...,dkM]
calculating the atomic fractions of M elements of the target to be detected according to the following formula:
wherein M is 12.
Claims (1)
1. A LIBS analysis method based on optical tomography simultaneous iterative reconstruction is characterized by comprising the following steps:
1) assuming that the number of elements needing quantitative analysis, namely the element dimension is M, sequencing side by side, preparing N standard samples for calibration, namely the sample dimension is N, wherein the N samples are solid and have equal size and size, the N standard samples contain the M elements in different proportions, the atomic fraction, namely the atomic number percentage, of each element is known, and the components in each sample are uniformly distributed;
2) constructing a multivariate LIBS quantitative analysis matrix equation, namely a projection matrix, a flow field physical quantity image matrix and a measurement matrix in optical tomography reconstruction
WF＝P
In the formula, W is a normalized spectral intensity matrix of a standard sample, which is equivalent to a projection matrix in optical tomography reconstruction; f is a correlation matrix, which is equivalent to a flow field physical quantity image matrix in optical tomography reconstruction, and P is a standard sample atomic fraction matrix, which is equivalent to a measurement matrix;
3) the normalized spectral intensity matrix W of the standard sample is constructed according to the following method:
LIBS detection is carried out on the N standard samples under the same test conditions and test parameters to obtain N LIBS spectrograms corresponding to the N standard samples, and normalization processing is carried out on the N LIBS spectrograms to obtain N normalized LIBS spectrograms; respectively taking k characteristic spectral lines for each element, and constructing a standard sample normalized spectral intensity matrix W with N rows by kM columns if the sample dimension N is greater than the spectral dimension kM:
the kM values in the first row in the normalized spectral intensity matrix represent the normalized spectral intensity values of the spectral lines represented by the M elements kM of the first standard sample; the kM values in the second row represent the normalized spectral intensity values of the M elements kM of the second standard sample representing the spectral lines; and so on …; kM values in the nth row represent the normalized spectral intensity values of the spectral lines for the nth standard sample M elements kM;
4) constructing a standard sample atomic fraction matrix P with N rows by M columns as follows:
the M values in the first row of the atomic fraction matrix represent the atomic fractions of the M elements of the first standard sample; the M values in the second row represent the atomic fractions of the M elements of the second standard sample; and so on …; the M values in the Nth row represent the atomic fractions of the M elements of the Nth standard sample;
5) the correlation matrix F, which reflects the correlation between W and P, can be expressed as:
the incidence matrix F is a matrix of kM rows by M columns, and the required solution kM2The F matrix can be obtained only by the unit value; column decomposing the incidence matrix F into M incidence vectors F1、F2、F3、...、FM(ii) a Performing column decomposition on the atomic fraction matrix P of the standard sample into M atomic fraction vectors P1、P2、P3、...、PM；
6) Converting the solution of the incidence matrix F into M incidence vectors F1、F2、F3、...、FMThe solution model is as follows:
Pi＝WFi+Ei
wherein i is 1,2,3iAs error vector, at N>In the case of kM, for FiThe solution of the correlation vector F is the solution of the over-determined equation, the error is minimum based on a certain optimization criterion, namely the optimal approximate solution under the optimization criterion is obtained, and the SIRT iterative algorithm based on the least square criterion is adopted to carry out the correlation vector FiAnd (3) solving:
Fi 0＝WTPi
Fi q+1＝Fi q+λ·WT(Pi-WFi q)
in the above formula, the superscript 0 represents the initial value; superscript T represents transposition; the superscript q represents the q-th iteration value; superscript q +1 represents the q +1 th iteration value; λ is a relaxation factor, and the magnitude of the value of λ represents the degree of tightness of the iterative constraint;
the termination conditions for the iteration are:
|Fi q+1-Fi q|2＜ε
epsilon is a very small number, and the value of epsilon is 0.001; after the iteration has terminated, FiThe last iteration value is FiThe solution result of (2);
7) all M relevance vectors FiAfter solving is completed, obtaining a correlation matrix F; LIBS detection is carried out on a target to be detected under the same test conditions as the N standard samples to obtain an LIBS spectrogram, and normalization processing is carried out on the LIBS spectrogram to obtain a normalized LIBS spectrogram of the sample to be detected; obtaining a normalized spectral intensity vector of M element kM representative spectral lines of the target to be detected:
D＝[d1,d2,d3,...,dkM]
calculating the atomic fractions of M elements of the target to be detected according to the following formula:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810091015.1A CN108414475B (en) | 2018-01-30 | 2018-01-30 | LIBS analysis method based on optical chromatography simultaneous iterative reconstruction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810091015.1A CN108414475B (en) | 2018-01-30 | 2018-01-30 | LIBS analysis method based on optical chromatography simultaneous iterative reconstruction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108414475A CN108414475A (en) | 2018-08-17 |
| CN108414475B true CN108414475B (en) | 2020-06-26 |
Family
ID=63126576
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810091015.1A Active CN108414475B (en) | 2018-01-30 | 2018-01-30 | LIBS analysis method based on optical chromatography simultaneous iterative reconstruction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108414475B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111445964B (en) * | 2020-03-27 | 2023-05-12 | 合肥金星智控科技股份有限公司 | Visualization method of component analysis result |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002507741A (en) * | 1998-03-20 | 2002-03-12 | コンシリィオ ナツィオナーレ デレ リチェルケ | Method for quantitative analysis of atomic components of materials by LIBS spectroscopy |
| CN101915753A (en) * | 2010-07-30 | 2010-12-15 | 浙江师范大学 | Genetic Neural NetworkQuantitative analysis method for laser induced breakdown spectroscopy based on gGenetic Neural Networkgenetic neural network |
| CN103398988A (en) * | 2013-08-08 | 2013-11-20 | 南昌航空大学 | LIBS metal element quantification method based on different element multi-ion-state comparison and correction |
| CN103411931A (en) * | 2013-07-30 | 2013-11-27 | 中国科学院上海技术物理研究所 | Remote LIBS element quantitative analysis method based on weighted multispectral calibration |
| CN103983617A (en) * | 2014-05-04 | 2014-08-13 | 华中科技大学 | Improved laser probe quantitative analysis method based on wavelet transform |
| CN104251846A (en) * | 2014-09-04 | 2014-12-31 | 清华大学 | Discriminant analysis combined laser-induced breakdown spectroscopy quantitative analysis method |
| CN104697964A (en) * | 2015-03-10 | 2015-06-10 | 西北大学 | Method for quantitative analysis of sulfur and phosphorus in steel and iron by combining random forest algorithm with laser induced breakdown spectroscopy |
| WO2015104049A1 (en) * | 2014-01-08 | 2015-07-16 | Universidad Publica De Navarra | Quantitative analysis method for analyzing the elemental composition of materials by means of libs technique |
| CN104865228A (en) * | 2015-06-02 | 2015-08-26 | 中国科学院上海技术物理研究所 | Quantitative laser-induced breakdown spectroscopy (LIBS) detecting method based on fusion entropy optimization |
| CN106290263A (en) * | 2015-05-19 | 2017-01-04 | 中国科学院沈阳自动化研究所 | A kind of LIBS calibration and quantitative analysis method based on genetic algorithm |
-
2018
- 2018-01-30 CN CN201810091015.1A patent/CN108414475B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002507741A (en) * | 1998-03-20 | 2002-03-12 | コンシリィオ ナツィオナーレ デレ リチェルケ | Method for quantitative analysis of atomic components of materials by LIBS spectroscopy |
| CN101915753A (en) * | 2010-07-30 | 2010-12-15 | 浙江师范大学 | Genetic Neural NetworkQuantitative analysis method for laser induced breakdown spectroscopy based on gGenetic Neural Networkgenetic neural network |
| CN103411931A (en) * | 2013-07-30 | 2013-11-27 | 中国科学院上海技术物理研究所 | Remote LIBS element quantitative analysis method based on weighted multispectral calibration |
| CN103398988A (en) * | 2013-08-08 | 2013-11-20 | 南昌航空大学 | LIBS metal element quantification method based on different element multi-ion-state comparison and correction |
| WO2015104049A1 (en) * | 2014-01-08 | 2015-07-16 | Universidad Publica De Navarra | Quantitative analysis method for analyzing the elemental composition of materials by means of libs technique |
| CN103983617A (en) * | 2014-05-04 | 2014-08-13 | 华中科技大学 | Improved laser probe quantitative analysis method based on wavelet transform |
| CN104251846A (en) * | 2014-09-04 | 2014-12-31 | 清华大学 | Discriminant analysis combined laser-induced breakdown spectroscopy quantitative analysis method |
| CN104697964A (en) * | 2015-03-10 | 2015-06-10 | 西北大学 | Method for quantitative analysis of sulfur and phosphorus in steel and iron by combining random forest algorithm with laser induced breakdown spectroscopy |
| CN106290263A (en) * | 2015-05-19 | 2017-01-04 | 中国科学院沈阳自动化研究所 | A kind of LIBS calibration and quantitative analysis method based on genetic algorithm |
| CN104865228A (en) * | 2015-06-02 | 2015-08-26 | 中国科学院上海技术物理研究所 | Quantitative laser-induced breakdown spectroscopy (LIBS) detecting method based on fusion entropy optimization |
Non-Patent Citations (4)
| Title |
|---|
| Self-adaptive reconstruction algorithm for;Xiong Wan;《Opt. Eng》;20040531;第1244-1250页 * |
| 基于光学层析技术的图像重建;刘桃丽;《光电技术应用》;20081231;第60-63页 * |
| 基于自由定标法的激光诱导击穿光谱定量化分析技术研究;郑剑杰;《万方学位论文》;20101213;全文 * |
| 基于遗传算法和偏最小二乘法的;邹孝恒;《分析化学(FENXI HUAXUE) 研究报告》;20150228;第181-186页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108414475A (en) | 2018-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Carminati et al. | Implementation and assessment of the black body bias correction in quantitative neutron imaging | |
| US6584413B1 (en) | Apparatus and system for multivariate spectral analysis | |
| US6675106B1 (en) | Method of multivariate spectral analysis | |
| Lisker et al. | Hot subdwarfs from the ESO Supernova Ia Progenitor Survey-I. Atmospheric parameters and cool companions of sdB stars | |
| Taggart et al. | Core-collapse, superluminous, and gamma-ray burst supernova host galaxy populations at low redshift: the importance of dwarf and starbursting galaxies | |
| Boeche et al. | SP_Ace: a new code to derive stellar parameters and elemental abundances | |
| CN109738413B (en) | Mixture Raman spectrum qualitative analysis method based on sparse nonnegative least square | |
| WO2016035626A1 (en) | Spectroscopic quantification method, and spectroscopic quantification device and program | |
| JP5150620B2 (en) | A method to determine the feasibility of the proposed structural analysis process | |
| Zhang et al. | A novel approach for simple statistical analysis of high-resolution mass spectra | |
| CN103543132B (en) | A kind of coal characteristic measuring method based on wavelet transformation | |
| Ripken et al. | The sensitivity of Cherenkov telescopes to dark matter and astrophysical anisotropies in the diffuse gamma-ray background | |
| CN108444980B (en) | L IBS quantitative analysis method based on algebraic reconstruction relevance vector solution | |
| Fernández et al. | The resolved chemical composition of the starburst dwarf galaxy CGCG007-025: direct method versus photoionization model fitting | |
| CN108414475B (en) | LIBS analysis method based on optical chromatography simultaneous iterative reconstruction | |
| CN107966499B (en) | Method for predicting crude oil carbon number distribution by near infrared spectrum | |
| CN104865228B (en) | The quantitative LIBS detection method solved based on fusion entropy optimization | |
| Tyler | The accuracy and precision of the advanced Poisson dead‐time correction and its importance for multivariate analysis of high mass resolution ToF‐SIMS data | |
| Karaçaylı et al. | Optimal 1D Ly α forest power spectrum estimation–III. DESI early data | |
| Morelli et al. | Automatic procedure for mass and charge identification of light isotopes detected in CsI (Tl) of the GARFIELD apparatus | |
| CN111896497B (en) | Spectral data correction method based on predicted value | |
| Gelb et al. | Statistically rigorous analysis of imaging SIMS data in the presence of detector saturation | |
| Streicher et al. | Sky subtraction for the MUSE data reduction pipeline | |
| Jones et al. | A bootstrap method for gain calibration and resolution determination of a lead-glass calorimeter | |
| CN108444981B (en) | LIBS quantitative solving method based on MART multiplicative reconstruction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |